AI Costs Surge as Vendors Shift to Usage-Based Pricing, Leaving Companies Scrambling

AI vendors have found someone to pay their infrastructure bills: You

Forrester warns that customers should brace for bigger software bills next year as software and AI vendors raise prices and pile on usage charges. Working from a survey of more than 2,600 business and technology decision-makers, the tech research company said software budgets were expected to rise "as vendors increase prices or add usage charges to pass their AI costs to customers." In the last six months, Anthropic, OpenAI, and GitHub have shifted some services away from flat-rate subscriptions toward usage-based billing, prompting cost concerns among users. Forrester added Microsoft to the list, citing its recent launch of the premium E7 license, which bolts M365 Copilot, Agent 365, and security tools onto E5. Last year, consultants Bain & Company estimated that the build cost for AI datacenters would hit $2 trillion by 2030. Forrester said that AI would drive increases in data and software spending, with 80 percent of decision-makers expecting those budgets to rise. Sharyn Leaver, chief research officer at Forrester, said: "The organizations that outperform in 2027 won't be those that spend the most on AI. They'll be the ones that invest in the foundations that make AI effective: trusted data, strong governance, organizational readiness, and the ability to continuously adapt as technology and customer behavior evolve." Forrester also found that personnel costs have yet to fall, despite the "AI washing of layoffs" in the tech industry. "While several tech giants, including Oracle, Microsoft, and Meta, have announced significant layoffs in recent months, IT staffing spend has not declined in recent years," the report said. Staffing accounted for 35 percent of IT budgets in 2025. For 2027, 67 percent of tech decision-makers expected to increase their staffing budget, while 23 percent said it would stay flat, and 10 percent expected it to decline. "The AI washing of layoffs will continue as vendors trim for financial and restructuring reasons. Guard against inflated promises that AI can replace employees across the board. Staffing for data/analytics-specific roles is expected to rise, with 68 percent of data technology decision-makers expecting this budget item to increase," the report said. Forrester said that organizations should adapt their FinOps practices to help manage the unpredictable costs associated with AI. "Traditional FinOps wasn't built for token-based, usage-driven AI costs, but that team is certainly best positioned to build these new capabilities and must make this leap in 2027. Fund runtime cost controls such as model routing, semantic caching, and usage guardrails to prevent runaway spend," the report recommended. In July, KPMG research found that nearly a third of corporate leaders reported difficulty understanding and controlling operating costs when implementing business AI at scale. "As usage-based pricing models become more common, many organizations are still building the capabilities required to forecast, monitor, and manage AI spending effectively," the consultancy said. ®

Experts warn software budgets could be set to soar as AI bills are on the rise

* Forrester analysts warn around four in five leaders and ITDMs envision having larger budgets in 2027 * Consumption-based AI pricing is making it harder to predict outlay * Targeted investment to improve data quality is key Forrester is predicting software budgets could be set to rise, with more than four in five leaders expecting to increase overall budgets over the next 12 months and 82% of tech decision-makers expecting larger budgets. While some of the extra cash could come as a result of increased confidence and readiness to spend on tech, the company's analysts warn that a shift in pricing structures could also be forcing companies to fork out more. This comes as software vendors shift from traditional per-seat licences to token or credit pricing, which introduces so many more variables including model selection, context size, output length and agent operating time, leading to far more unpredictable outgoings. The real reason businesses are preparing to spend more on software "Business leaders are no longer planning for a return to stability - they're planning for a future where volatility is a constant," Chief Research Officer Sharyn Leaver noted. Recent shifts from major AI providers all point toward this emerging pricing model becoming the norm, with GitHub moving its Copilot plants to usage-based billing in June and OpenAI adding pay-as-you-go Codex seats in April. Anthropic also recently removed Fable 5 from its standard subscriptions and seat-based models over difficult-to-predict demand, but set out plans to reintroduce it where capacity permits. Acknowledging these major shifts, Forrester's report reveals two areas where companies can increase their budgets for 2027 - building machine-readable context and enterprise knowledge, and increasing brand visibility in answer engines. The report also hints at the major role AI can play in marketing and customer-facing experiences, and the potential use that synthetic data can provide subject to testing. All in all, it's more about targeting investments rather than throwing cash at the problem, as Leaver concludes: "The organizations that outperform in 2027 won't be those that spend the most on AI. They'll be the ones that invest in the foundations that make AI effective." Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds.

The paradox of the AI invoice

Artificial intelligence budgets are skyrocketing. In a compressed macroeconomic environment where corporate expenditure is under intense scrutiny, soaring AI costs are a boardroom vulnerability. As agentic AI reshapes the enterprise Software-as-a-Service landscape, a critical debate has emerged over how to price this technology. The prevailing narrative suggests that as autonomous AI agents assume workflows traditionally executed by humans, the legacy seat-based pricing model will naturally dissolve, replaced by consumption-based billing. After all, if an algorithmic agent is executing the work, the concept of a software seat feels fundamentally obsolete. While this argument is compelling on paper, it overlooks the core principles of corporate procurement and budgeting. Developers and product teams prize the agility of consumption-based models, but chief financial officers operate on a foundation of fiscal predictability. For them, a budget is an immovable commitment. The friction between AI innovation and fiscal predictability is fast approaching a stalemate in software procurement. AGILITY VERSUS FORECASTING Consumption-based pricing promises ultimate transparency, aligning cost directly with realized value. In practice, however, an uncapped billing system can quickly transform into a budgeting nightmare, particularly for mid-market enterprises operating on finite technology allocations. Two major reasons why uncapped billing systems create a business challenge are:

AI Was Supposed to Save Companies Money. Instead, It's Blowing Up Budgets in a Big Way

A survey from KPMG finds business owners are aghast at their bills for AI, now that many AI companies have shifted to a usage-based model. The accounting firm spoke with 2,145 executives around the world. And one-third said they had a limited understanding of usage costs. AI companies used to charge corporate clients a flat rate, but as compute costs have increased, many major operators are switching to a different model to help control costs. That wasn't factored into some executives' decisions to go all-in on the technology. "AI is now as much a financial management priority as it is a technology one," Rob Fisher, global head of advisory at KPMG, said in a statement. "The real risk isn't investing in AI but doing so without cost visibility and an understanding of the economics of AI. Organizations that have visibility into their costs and maintain strong oversight are the ones translating AI investment into real, measurable value."

The hidden meter running on your AI

Most companies can tell you whether their artificial intelligence is online. Far fewer can tell you what it spent in the last hour, or why. They are content to find out at month's end, when the invoice lands. It's a mistake too many make. Treating AI cost as something you reconcile after the fact is how good products become unprofitable ones. The gap between a system that looks healthy and a system that is quietly expensive is the part of the AI era that has caught many leaders off guard. For decades, software costs were boring in the best way. You bought software, the price was roughly fixed, and you could plan a year ahead. AI broke that arrangement. Every interaction with a model consumes tokens, the small units of text it reads and writes, and you pay for each. With usage-based pricing, spend now moves with behavior rather than with a contract. The more your customers use the AI agent, and the more steps AI takes to answer them, the more it costs your organization. The meter is always running, and most organizations cannot see it move until it is too late to do anything about it. CHEAPER BY THE UNIT, BIGGER BY THE BILL The confusing part is that AI keeps getting cheaper to use while the bills keep climbing. Gartner forecasts that by 2030, running inference on a one-trillion-parameter model will cost providers more than 90% less than it did in 2025. Yet, enterprise AI spending is rising anyway, because consumption is growing faster than prices are dropping. When something becomes cheaper and more useful, people use far more of it.